A variety of medical imaging modalities, for example computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound, have become well-known techniques for obtaining medical imaging data representative of a patient or other subject for diagnostic or other purposes.
It is known to obtain multiple medical imaging data sets of the same patient or other subject by performing measurements at different times, under different conditions or using different modalities. Such multiple medical imaging data sets often have different co-ordinate systems, such that the same anatomical feature of the subject will appear at positions having different co-ordinates in the different medical imaging data sets (for example, in a simple case, due to the patient or other subject having a different relative position within the scanner when the different imaging data sets were obtained).
It is known to register different medical imaging data sets, for example different medical imaging data sets for the same patient or other subject obtained at different times, to obtain registration data that comprises or represents a transformation of co-ordinates for one or both of the medical imaging data sets. By transforming the co-ordinates of one or both medical imaging data sets it can be provided that the medical imaging data sets are aligned such that the same anatomical features from the medical imaging data sets appear at substantially the same, or corresponding, positions in a common co-ordinate system.
It is known to perform registrations manually or automatically using known analysis techniques. Different types of registration transformation may be used, for example (in order of increasing degrees of freedom) rigid body, affine, or non-rigid.
A rigid body registration in this context may comprise a registration in which the co-ordinates of data points in one data set are subject to rotation and/or translation in order to register the data set to another data set. An affine registration in this context may comprise a registration in which the coordinates of data points in one dataset are subject to rotation, and/or translation, and/or scaling and/or shearing in order to register the dataset to another dataset. Thus, a rigid registration may be considered to be a particular type of affine registration.
Non-rigid registrations can provide different displacements for each voxel of the data set to be registered and can, for example, use non-linear transformations, in which the coordinates of data points in one dataset are subject to flexible deformations in order to register the data set to another data set. Non-linear transformations may in some cases be defined using vector fields such as warp fields, or other fields or functions, such as B-splines, defining an individual displacement for each voxel in a three-dimensional data set.
Chronic obstructive pulmonary disease (COPD) was the fourth leading cause of death in 2011, responsible for an estimated 3 million deaths worldwide. Currently, diagnosis is made primarily using non-imaging methods, with severity rated using a single measure for both lungs known as the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity score. Although there is currently no cure for COPD, appropriate early treatment can slow progression and improve the patient's quality of life. COPD can include one or more of a variety symptoms and conditions, for example emphysema, bronchitis, and functional small airways disease (fSAD).
It has been suggested to use imaging methods to diagnose or monitor COPD. In particular, it has been suggested to diagnose COPD based on two lung CT scans: one acquired at full inspiration, the other at full expiration. Inspiration scan voxels with CT intensity values less than a first threshold are identified as representing emphysema tissue. Expiration scan voxels with CT intensity values less than a second threshold are considered to indicate gas trapping. Using those thresholds and a parametric response map that represents the combinations of CT intensity values from both the expiration and inspiration scans, normal tissue, emphysema, and a category of functional small airways disease (fSAD) for tissue where there is gas trapping but not emphysema, are determined based on the CT intensity values. The known method is based on comparisons of the CT intensity values to thresholds.